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Mathematics For Machine Learning Course

Mathematics For Machine Learning Course
from 4 to 360h flexible workload
certificate recognized by MEC

What will I learn?

ናይ ንግዲ ኢንተለጀንስ ክእለትካ/ክእለትኪ ኣብ'ዚ ትምህርቲ ሒሳብ ንማሽን መምሃሪ ብምውሳድ ኣዕብዮ/ኣዕብይዮ። ዳታ ምድህሳስ ብምክያድ፡ እንታይ ከምዘለዎም ንምፍላጥን (outliers) ከምኡ'ውን ዘየለዉ ነገራት ከመይ ጌርካ ከም ትቆጻጸሮ/ትቆጻጸሪ ተምሃር። ቅድመ ምድላው ዳታን፡ መምዘንን ከመይ ጌርካ ኸም እትሰርሖን (normalization)፡ ከምኡ'ውን ነቶም ዘለዉ ከመይ ጌርካ ኸም እትቆጻጸሮም/ትቆጻጸሪዮም (outlier treatment) ተምሃር፡ እዚ ድማ ሞዴልካ ዝያዳ ትኽክል ክኸውን ይሕግዝ። ነቶም ንግዜ ተኸታታሊ ዝኾኑ ማሽን መምሃሪ ኣልጎሪዝምታት ተምሃር፡ ከም ናይ ውሳኔታት ኣግራብን (decision trees) ኣሪማን (ARIMA)። ኣብ ምህንድስና መለለዪታት (feature engineering)፡ ስልቲታት ምምሕያሽን (optimization techniques)፡ ከምኡ'ውን ሞዴል ከመይ ከም እትገምግም/ትገምግሚ (model evaluation) ክእለትካ/ክእለትኪ ኣመሓይሽ/ኣመሓይሺ። እዚ ኮርስ'ዚ ኣብ ናይ ሓቂ ዓለም ንትጥቀመሎም ግብራዊን ጥዑይን ክእለታት የዕጥቀካ/የዕጥቀኪ።

Elevify advantages

Develop skills

  • መዋቅር ዳታ ምልላይ (Master data structures)፡ ነቲ ዝተሓላለኸ ዳታታት ብትኽክል ተንትኖን ተረድኦን።
  • ንዘይተለምዱ ነገራት ምፍላጥ (Detect outliers)፡ ንትኽክለኛነትን ምትእምማንን ናይ ዳታ ንምዕባይ፡ እንታይ ከምዘሎ ምፍላጥ።
  • ንግዜ ተኸታታሊ ሞዴላት ተጠቃቀም (Apply time series models)፡ ኣሪማን (ARIMA) ከምኡ'ውን ኤል-ኤስ-ቲ-ኤም (LSTM) ንግቡጽ ትንበያ ተጠቐም።
  • ኣልጎሪዝምታት ኣመሓይሽ (Optimize algorithms)፡ ንውጽኢታዊ ስልጠና ሞዴል፡ ናይ ደረጃ ምውራድ ስልቲ ተጠቐም (Implement gradient descent).
  • መለለዪታት ምህንድስና (Engineer features)፡ ንውጽኢት ሞዴል ንምምሕያሽ፡ ናይ ብዙሕነት መለለዪታት ፍጠር (Create polynomial features).

Suggested summary

Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.
Workload: between 4 and 360 hours

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